Forecasting US real private residential fixed investment using a large number of predictors

This paper employs classical bivariate, slab-and-spike variable selection, Bayesian semi-parametric shrinkage, and factor-augmented predictive regression models to forecast US real private residential fixed investment over an out-of-sample period from 1983Q1 to 2005Q4, based on in-sample estimates for 1963Q1–1982Q4. Both large-scale (188 macroeconomic series) and small-scale (20 macroeconomic series) slab-and-spike variable selection, and Bayesian semi-parametric shrinkage, and factor-augmented predictive regressions, as well as 20 bivariate regression models, capture the influence of fundamentals in forecasting residential investment. We evaluate the ex post out-of-sample forecast performance of the 26 models using the relative average mean square error for one-, two-, four-, and eight-quarter-ahead forecasts and test their significance based on the McCracken (2004, J Econom 140:719–752, 2007) mean-square-error F statistic. We find that, on average, the slab-and-spike variable selection and Bayesian semi-parametric shrinkage models with 188 variables provides the best forecasts among all the models. Finally, we use these two models to predict the relevant turning points of the residential investment, via an ex ante forecast exercise from 2006Q1 to 2012Q4. The 188 variable slab-and-spike variable selection and Bayesian semi-parametric shrinkage models perform quite similarly in their accuracy of forecasting the turning points. Our results suggest that economy-wide factors, in addition to specific housing market variables, prove important when forecasting in the real estate market.